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NBER WORKING PAPER SERIES
SUBJECTIVE WELL-BEING, INCOME, ECONOMIC DEVELOPMENT AND
GROWTH
Daniel W. SacksBetsey Stevenson
Justin Wolfers
Working Paper 16441http://www.nber.org/papers/w16441
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts
Avenue
Cambridge, MA 02138October 2010
The views expressed herein are those of the authors and do not
necessarily reflect the views of theNational Bureau of Economic
Research.
NBER working papers are circulated for discussion and comment
purposes. They have not been peer-reviewed or been subject to the
review by the NBER Board of Directors that accompanies officialNBER
publications.
© 2010 by Daniel W. Sacks, Betsey Stevenson, and Justin Wolfers.
All rights reserved. Short sectionsof text, not to exceed two
paragraphs, may be quoted without explicit permission provided that
fullcredit, including © notice, is given to the source.
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Subjective Well-Being, Income, Economic Development and
GrowthDaniel W. Sacks, Betsey Stevenson, and Justin WolfersNBER
Working Paper No. 16441October 2010JEL No. I31,I32,O11
ABSTRACT
We explore the relationships between subjective well-being and
income, as seen across individualswithin a given country, between
countries in a given year, and as a country grows through time.
Weshow that richer individuals in a given country are more
satisfied with their lives than are poorer individuals,and
establish that this relationship is similar in most countries
around the world. Turning to the relationshipbetween countries, we
show that average life satisfaction is higher in countries with
greater GDP percapita. The magnitude of the satisfaction-income
gradient is roughly the same whether we compareindividuals or
countries, suggesting that absolute income plays an important role
in influencing well-being. Finally, studying changes in
satisfaction over time, we find that as countries experience
economicgrowth, their citizens’ life satisfaction typically grows,
and that those countries experiencing morerapid economic growth
also tend to experience more rapid growth in life satisfaction.
These resultstogether suggest that measured subjective well-being
grows hand in hand with material living standards.
Daniel W. SacksThe Wharton SchoolUniversity of Pennsylvania3620
Locust WalkPhiladelphia, PA [email protected]
Betsey StevensonThe Wharton SchoolUniversity of Pennsylvania1454
Steinberg - Dietrich Hall3620 Locust WalkPhiladelphia, PA 19104and
[email protected]
Justin WolfersBusiness and Public Policy Department
Wharton SchoolUniversity of Pennsylvania3620 Locust WalkRoom
1456 Steinberg-Deitrich HallPhiladelphia, PA 19104-6372and
[email protected]
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I. Introduction
Does economic growth improve the human lot? 1 Using several
datasets which collectively
cover 140 countries and represent nearly all of the world’s
population, we study the relationship
between subjective well-being and income, identifying three
stylized facts. First, we show that
within a given country, richer individuals report higher levels
of life satisfaction. Second, we
show that richer countries on average have higher levels of life
satisfaction. Third, analyzing the
time series of countries that we observe repeatedly, we show
that as countries grow, their citizens
report higher levels of satisfaction. Importantly, we show that
the magnitude of the relationship
between satisfaction and income is roughly the same across all
three comparisons, which
suggests that absolute income plays a large role in determining
subjective well-being.
These results overturn the conventional wisdom that there is no
relationship between
growth and subjective well-being. In a series of influential
papers, Easterlin (1973, 1995, 2005a,
2005b) has argued that economists’ emphasis on growth is
misguided, because he finds no
statistically significant evidence of a link between a country’s
GDP and the subjective well-being
of its citizens. This is despite the fact that Easterlin and
others (e.g. Layard 1980) have found
that richer individuals in a given country report higher levels
of well-being. Researchers have
reconciled these discordant findings, together called the
Easterlin Paradox, by positing that well-
being is determined by relative, rather than absolute, income.
By this view, individuals want
only to keep up with the Joneses. If true, the Easterlin Paradox
suggests that focusing on
economic growth is futile; when everyone grows richer, no one
becomes happier. A related
concern, voiced for example by Di Tella and MacCulloch (2010) is
that subjective well-being
1 This paper revisits—and hopefully clarifies and
simplifies—many of the findings originally described in
Stevenson
and Wolfers (2008)
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adapts to circumstance. If correct, this argument implies that
long run growth makes people no
better off because their aspirations and expectations grow with
their income. A third concern is
that, even if well-being rises with income for the very poor,
individuals eventually reach a
satiation point, above which further income has no effect on
well-being (Layard 2005). Yet in
this paper, we present evidence that well-being rises with
absolute income, period. This
evidence suggests that relative income, adaptation and satiation
are of only secondary
importance.
Subjective well-being is multifaceted; it includes both how
happy individuals are at a
point in time and how satisfied they are with their lives as a
whole (Diener 2006). In section II
we briefly discuss relevant background information on the
measurement of subjective well-
being. Throughout this paper, we focus on life satisfaction,
which is the variable that is both
most often measured, and that has been the focus of much of the
existing literature (even as
economists have often referred to these satisfaction questions
as measuring “happiness.”)
Although life satisfaction is the focus of this paper, we
consider a variety of alternative measures
of subjective well-being and show that they also rise with
income.
In section III we demonstrate that richer individuals are more
satisfied with their lives,
and that this finding holds across 140 countries, and several
datasets. Across each of these
countries, the relationship between income and satisfaction is
remarkably similar. Our graphical
analysis suggests that subjective well being rises with the log
of income. This functional form
implies that a 20 percent rise in income has the same impact on
well-being, regardless of the
initial level of income: going from $500 to $600 of income per
year yields the same impact on
well-being as going from $50,000 to $60,000. This specification
is appealing on theoretical
grounds because a standard assumption in economics is that the
marginal impact of a dollar of
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income is diminishing. Indeed, estimating well-being as a
function of log income fits the data
much better than the simple linear function of income emphasized
by previous authors, and this
hold whether we are making comparisons across individuals,
across countries, or over time. All
of our formal analyses therefore involve the log of income
rather than its level, although we
present scatter plots and non-parametric fitted values to allow
the reader to assess the functional
form for herself.
In section IV, we turn to the cross country evidence. Using
larger data sets than previous
authors have examined, we find an economically and statistically
significant relationship
between average levels of satisfaction in a country and the log
of GDP per capita. The data also
show no evidence of a satiation point: the same linear-log
satisfaction-income gradient we
observe for poor and middle-income countries holds equally well
for rich countries; it does not
flatten at high income.
Whereas Easterlin (1974) had argued that the relationship
between well-being and
income seen within countries was stronger than the relationship
seen between countries, and that
this provided evidence for the importance of relative income,
our evidence undermines the
empirical foundation for this claim. Instead, we show that the
relationship between income and
well-being is similar both within and between countries, thereby
suggesting that absolute income
plays a strong role in determining well-being, and relative
income is a less important influence
than had been previously believed.
In section V we turn to the time series evidence. While the
within- and between- country
comparisons cast doubt on the Easterlin Paradox, they do not by
themselves tell us whether
economic growth in fact translates into gains in subjective
well-being. This question has
challenged researchers for some time because of a lack of
consistent time series data on
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subjective well-being. We analyze the time series movements in
subjective well-being using two
sources of comparable repeated cross-national cross-sections.
Each data sets spans over two
decades and covers dozens of countries.
In analyzing the time series data we can subject the relative
income hypothesis to a test: if
notions of a good life change as the income of one’s fellow
citizens grow, then we should see
only a modest relationship between growth in satisfaction and
growth in average income, relative
to our point-in-time estimates. We present economically and
statistically significant evidence of
a positive relationship between economic growth and rising
satisfaction over time, although
limited data mean that these estimates are less precise than are
those from the within- or
between- country regressions. The magnitude of the estimated
gradient between satisfaction and
income in the time series is similar to the magnitude of the
within- and between-country
gradients. These results suggest that raising the income of all
does indeed raise the well-being of
all.
Finally, in section VI we turn to alternative measures of
subjective well-being, showing
that they too rise with a country’s income. We find that
happiness is positively related to per
capita GDP across a sample of 69 countries. We then show that
additional, affect-specific
measures of subjective well-being, such as whether an individual
felt enjoyment or love, or did
not feel pain, are all higher in countries with higher per
capita GDP. Our finding that subjective
well-being rises with income is therefore not confined to an
unusual data set or a particular
indicator of subjective well-being.
Taken together, these new stylized facts suggest that subjective
well-being, however
measured, rises with income. Other recent papers have noted this
as well. Deaton (2008) finds
that individuals in richer countries have both higher levels of
subjective well-being and better
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health. Stevenson and Wolfers (2008), performing an analysis
parallel to this one–albeit using
slightly different methods2–report similar findings to those
described here, and discuss in detail
why previous researchers failed to identify the strong link
between subjective well-being and
income.
II. Background on Subjective Well-Being
Subjective well-being has many facets. Some surveys, such as the
World Values Survey, ask
respondents about their life satisfaction, asking, “All things
considered, how satisfied are you
with your life these days?” The Gallup World Poll includes a
variant of this question in which
respondents were shown a picture and told “Here is a ladder
representing the ‘ladder of life.’
Let’s suppose the top of the ladder represents the best possible
life for you; and the bottom, the
worst possible life for you. On which step [between 0 and 10] of
the ladder do you feel you
personally stand at the present time?” This question, which we
refer to as the satisfaction ladder,
is a form of Cantril’s “Self-Anchoring Striving Scale” (Cantril
1965). Other surveys ask about
happiness directly (“Taking all things together, how would you
say things are these days—would
you say you’re very happy, fairly happy, or not too happy?”).
Gallup also asks a battery of more
specific questions, ranging from “Were you proud of something
you did yesterday” to “Did you
experience a lot of pain yesterday?” Whereas the satisfaction
question invites subjects to assess
the entirety of their well-being, the more-specific questions
hone in on affect; they measure
feelings rather than assessments (Diener 2006). In this paper,
we will largely focus on life-
satisfaction, although in section VI we turn to examining the
relationship between income and
particular components of well-being.
2 Compared with that earlier study, some of the results in this
paper differ because we consider a simpler and more
transparent scaling of subjective well-being, and we use some
more recent data from the Gallup World Poll.
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We focus on satisfaction rather than other measures of
subjective well-being, such as
happiness, for two reasons. First, we would like to use as many
data sets as possible to assess the
relationship between subjective well-being and income, and life
satisfaction and the satisfaction
ladder are more commonly measured than any other measure.
Second, the previous literature
documenting the Easterlin Paradox (including Easterlin 1974,
1995, 2005a, 2005b, 2009) has
largely focused on life satisfaction questions (even as
researchers have tended to label these
analyses of “happiness”). Thus we focus our attention on
analyzing similar questions for direct
comparability with the previous literature. However, we assess
the income-happiness link in
detail in section VI along with other more affective measures of
well-being and the results are
similar to the income-satisfaction link.
Subjective well-being data are useful only if the questions
succeed in measuring what
they intend to measure. Economists have traditionally been
skeptical of subjective data because
they lack any objective anchor and because some types of
subjective data, such as contingent
valuations, suffer from severe biases (e.g. Diamond and Hausman
1994). These objections apply
to subjective-well being data, but a variety of evidence points
to a robust correlation between
answers to subject-well being questions and alternative measures
of personal well-being. For
example, self-reported well-being is correlated with physical
measures such as heart rate and
electrical activity in the brain as well as sociability and a
propensity to laugh and smile (Diener
1984). Self-reported well-being is also correlated with
independently ascertained friends’ reports
and with health and sleep quality (Diener, Lucas and Scollon
2006; Kahneman and Krueger
2006). Measures of subjective well-being also tend to be
relatively stable over time and they
have a high test-retest correlation (Diener and Tov 2007). If
people answered subjective well-
being questions without rhyme or reason, we would not see these
correlations across questions
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and people and over time. Individual subjective well-being data
therefore likely are anchored by
actual well-being.
Subjective well-being data lack a natural scale and are reported
differently across data
sets. For example happiness questions often ask respondents to
choose a level of happiness from
“very happy” to “very unhappy”, with one or two nominal values
in between. Life satisfaction
can be measured on a similar scale, or on a ladder of life with
ten or eleven rungs. In order to
compare answers across surveys, we convert all subjective well
being data into normalized
variables, subtracting the sample mean and dividing by the
sample standard deviation.
Whenever we report the subjective well-being-income gradient,
therefore, we are effectively
reporting the average number of standard deviation changes in
subjective well-being associated
with a one unit change in income (or log income). This rescaling
has the disadvantage of
assuming that the difference between any two levels of life
satisfaction is equal, although in fact
the difference between the fifth and sixth rung on the ladder of
life may be very different from
the difference between the ninth and tenth. There are many
alternative ways to standardize the
scale of subjective well-being; Stevenson and Wolfers (2008) use
an ordered probit and show
that the results we discuss here are robust to alternative
approaches.3
3 In Stevenson and Wolfers (2008), we estimated well-being
aggregates as the coefficients from an ordered probit of
well-being on country fixed effects, which yielded very similar
estimates. The most important difference is that the
ordered probit scales differences relative to the standard
deviation of well-being conditional on country dummies,
while the simpler normalization in this paper scales differences
relative to the (larger) unconditional standard
deviation of well-being. Given that country fixed effects
account for about 20% of the variation in well-being (that
is, R2≈0.2 in an OLS regression of satisfaction on country fixed
effects), this simpler normalization will tend to yield
estimates of the well-being–income gradient that are about
nine-tenths as large (√1 − �� ≈ 0.9).
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III. Within-Country Estimates of the Satisfaction-Income
Gradient
We begin our study of life satisfaction and income by comparing
the reported satisfaction
of relatively rich and less rich individuals in a given country
at a point in time. Many authors
have found a positive and strong within-country relationship
between subjective well being,
measured in various ways, and income. For example, Robert Frank
argues for the importance of
income as follows: “When we plot average happiness versus
average income for clusters of
people in a given country at a given time . . . rich people are
in fact a lot happier than poor
people. It’s actually an astonishingly large difference. There’s
no one single change you can
imagine that would make your life improve on the happiness scale
as much as to move from the
bottom 5 percent on the income scale to the top 5 percent”
(Frank 2005, p. 67). We confirm this
relationship, and, taking advantage of the enormous size of many
of our data sets, estimate
precisely the magnitude of the within-country
satisfaction-income gradient.
We assess the relationship between satisfaction and income by
estimating lowess
regressions of satisfaction against the log of household income.
Lowess regression effectively
estimates a separate bivariate regression around each point in
the data set, but weights nearby
points most heavily (Dinardo and Tobias 2001). Traditional
regression analysis imposes a linear
relationship, while the lowess procedure allows researchers to
study the functional form of the
relationship between two variables, such as life satisfaction
and the log of income.
In Figure 1, we plot the lowess estimate of the relationship
between the satisfaction
ladder score and the log of household income for each of the
largest twenty five countries in the
world (estimated separately), using data from the Gallup World
Poll.4 (Analyzing income per
equivalent household yields similar conclusions.) Satisfaction
scores are shown both as their raw
4 We are using a more recent vintage of the Gallup World Poll
than Stevenson and Wolfers (2008), incorporating
data made available through October 13, 2008.
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(0-10) scores on the left axis, and in their standardized form
(obtained by subtracting the whole
sample mean and dividing by the standard deviation) on the right
axis. To ease comparison with
subsequent figures, the standardized satisfaction scale and the
income scale are kept
approximately constant in the various charts throughout the
paper.
Figure 1 reveals the well-known finding that richer citizens of
a given country are more
satisfied with their life. For most countries, this plot reveals
that satisfaction rises linearly with
the log of income (as the horizontal axis is on a log scale).
Moreover, the gradient is similar
across countries, with the estimated line for each country
looking like parallel shifts of each
other. In spite of the enormous differences among these
countries, the relationship between
income and life satisfaction is remarkably similar across these
countries. Finally, we note that
this figure provides no evidence of satiation. While some have
argued that, above a certain
point, income has no impact on well-being, in these countries we
see that the curve is just as
steep at high levels of income as at low levels.
While these 25 countries account for the majority of the world’s
population, Gallup
polled individuals in 132 countries, making their poll the
widest survey of subjective well-being
ever undertaken. We summarize and quantify the relationship
between well-being and income
by pooling data from all the countries in our data sets and
estimating regressions of the following
form:
����������� ������������ = � ���� !"#�$% + '�!(�)�(
*+,�(-���.���) + /��0 + 1�� (1)
where i indexes individuals; c indexes countries; Income is
self-reported household income; and
X is a vector of individual-level controls including sex, a
quartic in age, and their interaction.
We include a country-specific intercept, ��, which adjusts for
differences in average satisfaction and income across countries,
thereby ensuring that the estimation results are driven by
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differences between rich and poor within each country. We denote
the coefficient of interest
'�!(�)�( *+ because it isolates the well-being- income gradient
obtained when comparing individuals within a country. In constrast
to much of the literature, we focus on the relationship
between subjective well-being and the log (rather than level) of
income. Our graphical evidence
supports this focus, since we observe that the
satisfaction-income gradient is approximately
linear-log.5
Table 1 presents the results, estimated separately in a variety
of datasets. We begin by
showing results from the 126 countries in the Gallup World Poll
with valid income data. Next,
we present results from the first four waves of the World Values
Survey which spans 1980-2004
and asks respondents to assess their life satisfaction on a 1-10
scale; we pool all waves and
include wave fixed effects to account for changes through time,
and changes in surveys between
waves. Stevenson and Wolfers (2008) document that for several
countries in this survey the
sampling frames are not nationally representative, and so we
drop these observations from all of
our analyses. Finally, we also analyze the 2002 Pew Global
Attitudes Survey, which covers 44
countries at all levels of development and uses the same ladder
of life question as Gallup.
The first column of Table 1 reports the regression results
without any controls (beyond
country fixed effects), and the estimated satisfaction-income
gradient ranges from 0.216 in the
World Values Survey, to 0.281 in the Pew Global Attitudes
Survey. In the second column we
add controls for age and sex, but our results remain similar.6
Within a given country, at a point
in time, people with higher income tend to report greater life
satisfaction.
5 Throughout the paper, therefore, when we refer to the
subjective well-being-income gradient, we mean the SWB-
log income gradient. 6 These estimates are slightly smaller than
those found in Stevenson and Wolfers (2008), which is partly due to
the
different normalization of satisfaction scores, and partly due
to the more recent vintage of the Gallup data analyzed
here.
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We would like to compare the estimates from equation (1) to
estimates of the cross-
country subjective well-being-income gradient, but to do so we
need to have a comparable
concept of income changes. While differences in income between
individuals within a country
reflect both transitory and permanent differences (and each has
different implications for
subjective well-being), income differences between countries are
likely to be much more
persistent, and indeed, close to entirely permanent.
How much of the cross-sectional variation in income within a
country represents
variation in permanent income? Standard estimates for the United
States suggest that around
two-fifths to a half of the cross sectional variation in annual
income comes from permanent
income (Haider 2001; Gottschalk and Moffit 1994).7 Our survey
asks about monthly income,
suggesting that the transitory share is larger; to be
conservative, we simply choose the upper end
of these estimates. We also need to convert the variation in
transitory income into its permanent
income-equivalent. If each extra dollar of transitory income
persists for only one year, then
people would be indifferent between one extra dollar of
transitory income, and a rise in
permanent income of about 5 cents (assuming a 5 percent discount
rate). Estimates of the
transitory component of annual income suggest that it doesn’t
all dissipate in one year; indeed,
the autoregressive process estimated by Haider (2001) suggests
that the permanent income-
equivalent of a $1 rise in transitory income would be about
twice the one-year value, or 10 cents.
Consequently a one dollar increase in income in the cross
section represents on average a 50 cent
rise in permanent income, plus a 50 cent rise in transitory
income, and this transitory income is
valued equivalently to a rise in permanent income of about 5
cents. This implies that to interpret
our estimated well-being–income gradient in terms of a $1 rise
in permanent income, our cross-
7 While our calculations will use these U.S. estimates as if
they are representative of the entire world, what is really
needed is similar studies for countries at different levels of
development.
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sectional estimates should be scaled up by about 80% (1/0.55).
We report the adjusted estimates
in the third column of Table 1, and they tend to be slightly
larger than 0.4.
We can also address this concern empirically by using an
instrumental variables strategy
designed to isolate variation in income that is likely
permanent. Specifically, we use a full set of
country×education fixed effects as instruments for permanent
income. The instrumental
variables estimates of '�!(�)�( *+—reported in the fourth column
of Table 1—are larger than the OLS estimates, and in the Pew and
Gallup data, they are close to the estimates we obtain after
making the permanent income adjustment. Education however is
very likely an imperfect
instrument for permanent income. While education is correlated
with permanent income, it likely
also directly impacts satisfaction, leading to upward bias on
the instrumental variables estimates
of '�!(�)�( *+ . Our reading of the within-country evidence,
therefore, is that the life satisfaction-log permanent income
gradient falls between 0.3 and 0.5.
We should not push these adjustments too hard, however. While it
seems straightforward
to think that permanent rather than transitory income determines
subjective well-being, in fact
direct evidence on this point suggests the opposite: subjective
well-being and the business cycle
move quite closely together. Stevenson and Wolfers (2008) report
that the output gap strongly
predicts subjective well-being, at least in the United States.
Wolfers (2003) shows this also
holds in Europe and across states in the United States.
IV. International Comparisons of Satisfaction and Income
The within-country relationship between income and life
satisfaction is well known and
admits at least two interpretations. The first interpretation is
that greater earning capacity makes
people satisfied with their lives: it purchases health care;
allows people to enjoy their leisure time
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with fancier food and TVs; and affords them freedom from
financial stress. A second
interpretation, however, is that people care less about money
than about having money relative to
some reference point (Easterlin 1973). One reference point is
their neighbor’s income, but other
reference points include a country (or the world’s) average
income. Or perhaps people use their
own previous income as a reference point. Under this view,
people are stuck on a “hedonic
treadmill;” as they grow richer, their expectations adapt to
their circumstances, and they end up
no more satisfied than they were before (Brickman and Campbell
1971). An alternative is that
an “aspiration treadmill” means that even as higher income
yields greater well-being, people may
eventually report no higher well-being than they previously
reported, because their expectations
grow with their income and well-being.
To sort out these interpretations, we turn to national data. If
all that matters for
satisfaction is one’s own income relative to one’s neighbor’s
income, or relative to mean national
income, then people in countries with high average income should
be no more satisfied than
people in poorer countries. Alternatively, to the extent that
national differences in income reflect
long-lasting differences, individuals should adapt to them (if
adaptation is important), so
adaptation predicts that the cross-country satisfaction-income
gradient should be small. On the
other hand, if absolute income matters (or if the relevant
reference point is mean global income),
then we would expect richer countries indeed to be more
satisfied. Thus we now assess the
satisfaction-income gradient across countries.
Our measure of average income in a country is GDP per capita,
measured at purchasing
power parity, to adjust for international differences in price
levels. These data come from the
World Bank’s World Development indicators data base; where we
are missing data, we turn to
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the Penn World Tables (version 6.2), and, failing that, the CIA
Factbook. For earlier years for
which data are unavailable, we turn to Maddison (2007).
Figure 2 plots average (standardized) life satisfaction data
drawn from each of the first
four waves of the World Values Survey, against GDP per capita
(shown on a log scale). The
figure shows both the OLS regression line and a non-parametric
(lowess) fit. As previously
noted, some of these observations were not based on nationally
representative surveys (typically
missing groups who might be expected to have low satisfaction),
and so we plot these with
squares rather than circles; they clearly lie far from the
regression line (which we calculate by
excluding them).8
The early waves of the survey, which contain mostly wealthy
nations, provide suggestive
but not overwhelming evidence for a positive link between the
log of GDP per capita and
subjective well-being. A researcher who mistakenly included the
non-representative countries
and who plotted satisfaction against the level rather than the
log of income could (erroneously)
fail to find a statistically significant relationship between
GDP per capita and subjective well-
being. Successive waves of the survey included more middle and
low-income countries, and the
relationship between income and well-being is clearer in the
later waves. The four waves span
25 years and 79 distinct countries with income ranging from less
than $1,000 to over $32,000 (in
2000 international dollars). This figure indicates a clearly
positive and approximately linear-log
relationship between life satisfaction and GDP.
Other data sets employing alternative measures of satisfaction
show a similar positive
relationship. Figure 3 plots the relationship between the
satisfaction ladder scores estimated
from the Pew Global Attitudes Survey and GDP per capita. The Pew
data show the same pattern
8 For more details about the World Values sampling frame and
which country-years include nationally
representative samples see Appendix B in Stevenson and Wolfers
(2008)
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as the World Values Survey data: richer countries exhibit higher
levels of satisfaction. The non-
parametric fit confirms the visual impression that there are no
important non-linearities:
satisfaction grows with log income at about the same rate
whether we focus on rich countries or
poor countries. This figure provides no evidence that the
satisfaction-log income gradient
diminishes as income grows, suggesting that no country is rich
enough to have hit a satiation
point, if such a point exists.
Although the Pew and World Values Survey results provide strong
evidence on the cross-
country link between satisfaction and income, neither survey has
quite the global coverage the
Gallup World Poll. In Figure 4, we plot the satisfaction ladder
scores against per capita GDP for
131 countries included in the Gallup World Poll (we exclude
Palestine, because we were unable
to find reliable GDP data). Every part of the GDP distribution
is well represented. This figure
confirms the by-now strong impression that richer countries have
higher levels of life satisfaction
than poorer countries, and that this relationship is
approximately linear-log. Indeed, the
correlation between average satisfaction scores in a country and
its log of GDP per capita is
above 0.8.
Because average well-being is rising in the log of average
income, our results suggest
that transferring a given amount of money from rich to poor
countries could raise life
satisfaction, because $100 is a larger percentage of income in
poor countries than rich countries.
The linear-log relationship revealed by the non-parametric fits
also provide evidence against
satiation: the relationship between well-being and income does
not diminish at high levels of
income, except to the extent implied by the log functional form.
If anything, the lowess curve
appears to tick upwards even more sharply at high levels of
GDP.
-
16
We quantify the magnitude of the satisfaction-income link in by
running similar
regressions to equation (1), but analyzing the satisfaction of
individuals i in country c as a
function of the log of average per capita income in their
country, instead of individual income
(and consequently we also drop the country fixed effects):
����������� ������������ = � + '*22#$2*"$ ln(567 8�� ��8���) +
/��0 + 1�� (2) Alternatively, we aggregate our satisfaction data up
into national averages, and run:
�������9��� ��9����9��::::::::::::::::::::::::::::::::::::� = �
+ '*22#$2*"$ ln(567 8�� ��8���) + 1� (3)We are interested in
'*22#$2*"$, which says by how much average satisfaction in a
country increases (in standard deviations) when the log of average
per capita income in a country is
higher.
These results, summarized in Table 2, confirm the impression
given by the graphical
analysis: all three of our data sets show a statistically
significant and positive relationship
between satisfaction and the log of GDP. These results suggest
that absolute income plays an
important role in explaining the relationship between
satisfaction and income. The magnitude of
the relationship is similar whether we estimate it in the
individual-level data or the national
averages, and whether or not we adjust for the differential age
and sex composition of
respondents. The coefficients on the log of average income vary
somewhat but are centered on
0.3 to 0.4.
This range is striking for its resemblance to the within-country
satisfaction-income
gradient. To emphasize the similarity, Figure plots data from
the Gallup World Poll. Each point
in the figure is a separate country, and for each country we
have plotted both a dot representing
the average satisfaction and income in that country, and an
arrow whose slope represents the
slope of the satisfaction-income gradient when comparing people
within that country. As we
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17
look across the 126 countries with valid household income data,
we find that there is no country
with a statistically significantly negative relationship between
satisfaction and income, and the
bulk of the lines all point in similar directions, and have a
similar slope. Importantly, these
slopes are roughly parallel to the dashed line, which shows the
slope one obtains when
comparing individuals within a country is similar to that
obtained when making comparisons
between country averages.
That is, our estimates of the satisfaction-income gradient are
similar whether estimated
within or between countries. Recall that the Easterlin Paradox
rested upon the belief that the
well-being-income gradient observed within countries is larger
than that seen between countries.
Earlier estimates of a statistically insignificant cross-country
relationship between average
satisfaction and average income reflected the fact that previous
researchers were looking at small
samples of fairly homogenous countries. It was the juxtaposition
of this statistically insignificant
finding with evidence of a statistically significant
well-being-income relationship that led
Easterlin to declare the data paradoxical. But the historical
absence of evidence for a
proposition—that richer countries are happier—should not have
been confused as being evidence
of its absence. And indeed, with our larger datasets, we find
statistically significant evidence
that high income countries are happier than their low income
counterparts. Instead, a claim
about the importance of relative income comparisons should rest
upon the quantitative
magnitudes of the estimated well-being-income gradients.
Indeed, the similarity of the within- and between- country
gradients has an important
interpretation that we can express more formally. Suppose
that:
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18
������������ = � + '*
-
19
An alternative story of reference-dependent preferences is based
on adaptation. By this
view, what matters for satisfaction is income relative to
expectations, and these expectations
adapt in light of recent experience. That is, economic growth
simply speeds up the pace of the
hedonic treadmill, as we all run faster, just to keep in place.
In turn, this implies that variation in
income that has persisted for sufficiently long for expectations
to adapt should be unrelated to
satisfaction. The differences in log GDP per capita shown in
Figures 3 through 5 are extremely
persistent, and across the 131 countries in the Gallup World
Poll, the correlation between the log
GDP per capita in 2006 shown in Figure 4, and its value in 1980
is 0.93. Consequently, this
theory suggests that these persistent cross-country differences
in GDP per capita should have
little explanatory power for satisfaction. The data clearly
falsify this hypothesis, too.
V. Satisfaction and Economic Growth
So far we have shown that richer individuals report higher life
satisfaction than poorer
individuals in a given country, and that on average citizens of
rich countries are more satisfied
with their lives than are citizens of poor countries. These
comparisons suggest that absolute
income plays an important role in determining well-being, but
they do not directly address our
central question: does economic growth improve subjective
well-being?
We answer this question by turning to the time series evidence
on life satisfaction and
GDP, which allows us to assess whether countries that experience
economic growth also
experience growth in subjective well-being. Estimating the time
series relationship between
GDP and subjective well-being is difficult because sufficiently
comparable data are rarely
available. For example, the General Social Survey in the United
States and the Life in Nation
surveys in Japan both surveyed subjective well being over a long
horizon, but both are afflicted
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20
by important changes in the wording and ordering of questions
that, if not recognized, can lead to
serious interpretation errors. Nevertheless, many scholars have
found that the US has not gotten
any happier over the past 35 years despite becoming wealthier, a
fact that Stevenson and Wolfers
(2009) note reflects a somewhat puzzling decline in female
happiness. In contrast, Japan, which
was once thought to have experienced little increase in
happiness over the post-war period, has in
fact experienced significant happiness gains that are similar in
magnitude to what one would
expect given the cross-sectional and cross-country relationships
between subjective well-being
and income. However, these happiness gains only become apparent
once changes in the survey
over time are taken into account (Stevenson and Wolfers 2008);
the failure to take account of
these changes had led many previous scholars astray (including
Easterlin 1995, 2005a).
We draw on two long-running data sets to examine the
relationship between subjective
well-being and economic growth: the World Values Survey and the
Eurobarometer. We analyze
the first four waves of the World Values Survey, which span 1980
to 2004 and cover 79 distinct
countries. Because the World Values Survey added many countries
in later waves, however, it is
not possible to make many comparisons of a given country.9 The
Eurobarometer survey has the
advantage that it has been surveying people in member nations of
the European Union virtually
continuously since 1973; however it has the disadvantage of only
covering relatively
homogenous countries. Unlike the other surveys, Eurobarometer
ascertains life satisfaction on a
four-point scale.10
Nine countries were included in the original Eurobarometer
sample. Analyzing data
through 1989, Easterlin (1995) concluded that the data failed to
show any relationship between
9 As noted earlier, some of the country samples in earlier waves
of the World Values Survey are not directly
comparable to later waves since their survey frames were
(intentionally) not nationally representative. Our analysis
focuses only on nationally representative samples. 10 For the
analysis, we keep West Germany and East German as separate
countries. For further details on the
Eurobarometer and our data procedures, see Stevenson and Wolfers
(2008).
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21
life satisfaction and economic growth. In Figure 6, we present
scatter plots of life satisfaction
and the log of GDP per capita for the nine countries Easterlin
analyzed. In the figure we include
as dark circles the original data he analyzed; hollow circles
denote data that have subsequently
become available through to 2007. The dark circles by themselves
do not always show a strong
relationship; however over the full sample, eight of the nine
countries show a positive
relationship between life satisfaction and growth, and six of
the nine slopes are statistically
significantly positive. The slopes range from -0.25 in Belgium
to 0.68 in Ireland. This re-
analysis not only suggests a positive relationship between
income and growth, but also hints at
the difficulty of isolating this relationship when data are
scarce.
The positive relationship between life satisfaction and economic
growth is not a feature
of Europe alone. In Figure 7, we turn to the World Values Survey
and plot changes in life
satisfaction against cumulative changes in real GDP. This survey
covers more countries, and at
very different levels of development, which allows us to see
whether populations become more
satisfied as their countries transition from low to moderate
income as well as moderate to high.
To keep comparisons clean, Figure 7 excludes countries in which
the sampling frame changed.
Each of the six graphs compares a different pair of waves. The
top row compares short
differences—the waves are separated by about five years—while
the bottom row shows longer
differences of 10-20 years. All six graphs indicate a positive
association between changes in
subjective well-being and changes in income; the estimated
gradients range from 0.22 between
waves I and III to 0.71 between waves I and II. The figure shows
that life satisfaction is more
sensitive to short run changes in income than to long run
changes, suggesting that business cycle
variation may be driving some of the association. An alternative
interpretation is that over time,
individuals adapt to their new circumstances or their
aspirations change, so that even though
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22
their material welfare is increasing their subjective well-being
gains from these increases recede
over time.
Figure 7 also reveals some potentially interesting (or
problematic) outliers. Korea, for
example, often falls outside the GDP change scale, but had only
a modest change in subjective
well-being; Hungary experienced very little growth, but had a
serious decline in life satisfaction.
In regression results reported below, we include these outliers,
but it is clear that excluding them
could change our estimates.
The comparisons in Figure 7 are particularly valuable because
all the comparisons are
between common pairs of waves, so they automatically adjust for
the various changes in the
survey—both question order and survey techniques—that occurred
between waves. Stevenson
and Wolfers (2008) document that these World Values Survey data
are strongly influenced by
these methodological changes, so this control is important.
Indeed, the influence of these
changes is large enough as to render naïve comparisons of raw
survey averages through time to
be problematic (Easterlin and Angelscu 2009; Easterlin and
Sawangfa 2008).
To distill the information from these figures into a single
estimate of the intertemporal
relationship between satisfaction and economic growth, we
estimate panel regressions of the
following form:
����������"� = '"�A$ %$#�$% ln(567"�) + � ���∈�� !"#�$% + � C" +
D"�"∈E*)$% (6)
where the time fixed effects, C" control for changes in question
order between waves, and the country fixed effects, ��, ensure that
only within-country changes through time drive the comparisons.
Panel A of Table 3 reports the results of estimating equation
(6) using the World Values
Survey and the Eurobarometer. We find a substantial and
statistically significant relationship
-
23
between life satisfaction and economic growth. The estimates are
not particularly precise,
however, and they differ considerably between the two data sets.
The satisfaction-income
gradient is 0.51 in the World Values Survey and 0.17 in the
Eurobarometer. In neither data set
can we reject the hypothesis that the true '"�A$ %$#�$% lies
between 0.3 and 0.4, the central estimate from the cross-country
regressions. We can however reject the null hypothesis that
'"�A$ %$#�$% = 0, which is the outcome suggested by the view
that relative rather than absolute income determines
well-being.
In order to assess whether these regressions are driven by
outliers, Figure 8 shows the
variation underlying our World Values Survey panel regression
estimates, while Figure 9
illustrates the variation underlying our Eurobarometer results.
Our panel regressions reflect
variation in satisfaction and log GDP per capita, stripped of
country and wave fixed effects.
Thus, the vertical axis shows residual satisfaction defined
by
��9����9���"F = ��9����9���":::::::::::::::::::: −
G[��9����9���":::::::::::::::::::|��J��K ��� L�M� ������], which is
obtained as the residual from a regression of satisfaction on
country and wave fixed
effects. Likewise the horizontal axis shows residual log
GDP,
ln(567�")F = ln(567�") − G[ln (567�")|��J��K ��� L�M� ������],
which is obtained from a similar regression in which log GDP is the
dependent variable. As can
be seen, when a country is experiencing relatively high levels
of GDP (relative to its country
average, and the estimated wave fixed effects), it also
experiences high levels of satisfaction. By
construction, our panel data regression coefficient in panel A
of Table 3, 'O"�A$ %$#�$%, is exactly equal to the slope of the
dashed bivariate regression line shown in each figure. These
figures
confirm that the results in Table 3 are not driven by a few
outliers; the points fit the regression
-
24
line well, and the correlation is quite strong. Equally, the
data in Figure 9 paint a somewhat
noisier picture for the Eurobarometer panel, although roughly
similar conclusions hold.
In obtaining these estimates, however, we have drawn on all the
variation in GDP in our
sample, including possibly high frequency changes to which
individuals do not have a chance to
adapt. If adaptation occurs slowly, it would be better to focus
on long run changes in GDP.
Indeed, Easterlin and Angelescu (2009) argue that only long run
economic growth can be used to
assess the relationship between growth and well-being.
So far, only the data plotted on the bottom row of Figure 7
speak to this point, showing
that even ten-year changes in GDP continue to influence life
satisfaction. However, each of
these comparisons is limited to the sets of countries that are
common to a pair of waves. Instead,
we can assess long differences for all countries by comparing
changes in ��9����9���"F and
ln(567�")F between the first and last time we observe a country
in the World Values Survey. We plot these variables against each
other in Figure 10 for each of the 56 countries in
World Values Survey that we observe multiple times. The average
difference in time between
first and last observations is about eleven years. (This number
is comparable to Easterlin and
Sawangfa’s notion of the “long run”—they require data spanning
at least ten years—but
somewhat lower than Easterlin and Angelescu’s twelve year
requirement.) The majority of
countries are located in the northeast and southwest quadrants,
and therefore their GDP and
satisfaction moved together (relative to wave fixed effects). A
notable number of countries,
however, lie in the northwest and southeast; their life
satisfaction and GDP move in opposite
directions. Even so, the correlation between these variables is
positive and remarkably strong,
given that we are analyzing first differences.
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25
In panel B of Table 3 we report the estimate of the relationship
between well-being and
growth obtained from regressing these long differences in
��9����9���"F against long
differences in ln(567�")F . We bootstrap our standard errors to
account for the uncertainty in generating residual satisfaction and
GDP.11 The coefficient is 0.47 and statistically significantly
different from zero, and with these long differences, once
again, we cannot reject the hypothesis
that the true '"�A$ %$#�$% lies between 0.3 and 0.4. Using these
same data (although including the observations from the
unrepresentative
national samples and not adjusting for wave fixed effects),
Easterlin and Sawangfa (2008, p.13)
argue that “the positive association between the change in life
satisfaction and that in GDP per
capita reported by Stevenson and Wolfers rests almost entirely
on the positively correlated V-
shaped movement of the two variables during the post-1990
collapse and recovery in the
transition countries.” In order to investigate this claim, we
separately estimate our panel
regressions and long differences for the sample of transition
countries only, and for all other
World Values Survey nations. While breaking the sample apart
like this reduces our statistical
precision, the key inferences remain the same in both samples:
the influence of GDP growth on
satisfaction is positive, statistically significantly different
from zero, and we cannot reject that
these coefficients lie between 0.3 to 0.4, and if anything, the
World Values Survey yields
estimates of the time series satisfaction-income gradient that
is somewhat larger. The critique
leveled by Easterlin and Sawangfa seems, quite simply,
wrong.
Figure 10 provides further evidence why estimating the
relationship between subjective
well-being and long run growth has challenged researchers. There
are indeed many countries
11 We bootstrap the two-step procedure as follows. For each
bootstrap iteration, we first compute the residuals as
described, and then regress ��9����9��F �" against ln (567�")F .
We perform 1000 iterations, and take the standard deviation of the
distribution of computed gradients as our estimated standard error
(after making a degrees-of-
freedom adjustment).
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26
which do not fit the general trend that growth in satisfaction
is correlated with GDP growth.
Bulgaria, the Ukraine, Venezuela, and Estonia all experienced
considerable declines in income,
with no accompanying decline in well-being. Furthermore, a
researcher, worried about outliers,
could easily drop a handful of influential countries from the
sample – like Russia, Hungary,
Slovenia, and Korea. Doing so clearly does not eliminate the
positive correlation between these
long differences, but removing these countries substantially
reduces the statistical power of the
regression, because these extreme cases involve so much of the
variation in ∆ ln(567�")F . When
we exclude these countries from our regression of long
differences, our estimate of '"�A$ %$#�$% remains positive and
comparable to other estimates at 0.26, but the standard error grows
to 0.15.
We repeat this exercise using the Eurobarometer data. The
advantage of these data is that
we have many observations for each country, which we can combine
to reduce the influence of
measurement error. Thus we construct long differences in the
Eurobarometer by taking averages
of ��9����9���"F and ln(567�")F for each country in each of the
decades 1973-1982, 1983-1992, 1993-2002, and 2003-2007. We then
construct decadal differences in satisfaction and
GDP by comparing adjacent decades, and plot these decadal
differences in Figure 11. Each point
represents a single decadal difference in satisfaction and GDP
for a given country. Many
countries experienced sluggish income growth but no relative
slowdown in subjective well-
being. Most of these countries are in Western Europe. For a
majority of countries, however,
GDP and satisfaction do move in the same direction, although the
correlation is much weaker
than in our previous estimates. The estimated
satisfaction-income gradient resulting from these
long differences, also reported in the right column of Table 3,
summarizes the results from this
figure. We find a marginally statistically significant gradient
of 0.28.
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27
Over all we find a positive but somewhat less precise
relationship between growth in
subjective well-being and growth in GDP. When we use all of the
time-series variation in GDP,
we find a well-being-income gradient that is similar to the
within-country and cross-sectional
gradients. When we estimate longer differences, the precision of
the relationship falls but the
point estimate is similar in magnitude. This remains true
whether we exclude potentially
problematic “transition” economies from the sample or not, or
whether we limit our attention to
long-run changes in income or not, or whether we analyze data
from the World Values Survey or
the Eurobarometer. None of our estimates using the full
variation in GDP allows us to reject the
hypothesis that '"�A$ %$#�$% lies between 0.3 and 0.4, the range
of our estimates of the static relationship between well-being and
income.
VI. Alternative Measures of Subjective Well-Being
Thus far, we have shown that there is a positive, statistically
significant, and
quantitatively important relationship between life satisfaction
and income, and that this
satisfaction-income gradient is similar in magnitude whether one
analyzes individuals in a given
country, countries at a point in time, or a given country over
time. But life satisfaction is not the
only measure of subjective well-being, and so we now turn to
considering the relationship
between various other measures of subjective well-being and
income. For brevity (and also due
to data availability), we will focus on cross-country
comparisons of these alternative indicators.
In Figure 12 we begin by studying happiness, showing the
cross-sectional relationship
between happiness and the log of GDP per capita, using data from
the fourth wave of the World
Values Survey. We follow the same graphing conventions as in
previous charts, showing the
national averages as both their average on their original four
point scale, and as standardized
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28
values (on the right axis). We also show both the regression
line (where the dependent variable
is the standardized measure of happiness) and the non-parametric
fit; this regression line shows a
positive and statistically significant relationship between
happiness and per capita GDP, although
the estimated happiness-income gradient is not as large as the
satisfaction-income gradient we
estimate in Table 2. The presence of two extreme outliers,
Tanzania and Nigeria, skews the
regression estimates considerably. These countries are
particularly puzzling because they are the
poorest in the sample, but they report among the highest levels
of happiness. They also have
much lower average life satisfaction—indeed, Tanzania is the
least satisfied of any country in
our sample. Perhaps there is a banal explanation for this
puzzle: survey documentation suggests
that there difficulties translating the happiness question in
Tanzania. Stevenson and Wolfers
(2008) discuss the happiness-income link more fully and find
very similar results to the
satisfaction-income link: happiness increases at any aggregation
of the data, and the magnitude
of the link is not much affected by the degree of
aggregation.
We turn now to alternative and more specific measures of
subjective well being. The
Gallup World Poll asks respondents about many facets of their
emotional health and daily
experience. For several experiences such as enjoyment, physical
pain, worry, sadness, boredom,
depression, anger or love, the Gallup poll asks, “Did you
experience [feeling] during a lot of the
day yesterday?” These questions sketch a psychological profile
of hundreds of thousands of
people spanning the world’s income distribution. In Figure 13,
we present scatter plots of the
probability that an individual in a given country experienced
various emotions yesterday, against
GDP per capita. The figure suggests that citizens of richer
countries are more likely to
experience positive emotions and less likely to experience
negative emotions. Enjoyment is very
highly correlated with GDP, while love is moderately correlated.
Physical pain, depression,
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29
sadness and anger all decline moderately with GDP.12 Worry
increases slightly with GDP,
although there is not a strong pattern.
The Gallup poll also probes respondents for an array of
sentiments about their day
yesterday, asking whether they: felt well rested, were treated
with respect, chose how to spend
their time, if they smiled or laughed a lot, were proud of
something they did, or ate good tasting
food. The daily experience questions, which uniformly measure
positive experiences, paint a
picture that is consistent with our analysis thus far. Figure 14
shows in each country the percent
of people who felt a certain way in the previous day. People in
richer countries are more likely to
report feeling better rested and respected, smiling more, and
eating good tasting foods than
people in poorer countries, although they are no more likely to
take pride in what they did or to
have learned something interesting.
These data point to a more nuanced relationship between
well-being and income. While
they give no reason to doubt that well-being rises with income,
they also suggest that certain
facets of well-being respond less to income than others. These
data hint at the possibility of
understanding which emotions and experiences translate into the
part of life satisfaction that is
sensitive to changes in income.
VII. Conclusions
This paper revisits the stylized facts on the relationship
between subjective well-being
and income. We find that within a given country, rich
individuals are more satisfied with their
lives than poorer individuals, and we find that richer countries
have significantly higher levels of
12 See Krueger, Stevenson, and Wolfers (2010) for a more
thorough exploration of the relationship between
experiencing pain and income.
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30
average life satisfaction. Studying the time series relationship
between satisfaction and income,
we find that economic growth is associated with increases in
life satisfaction.
The key innovation is this paper is to focus explicitly on the
magnitude of the subjective
well-being-income gradient (rather than its statistical
significance), while also bringing the
greatest quantity of data to bear on these questions. We show
that the within-country, between-
country, and over-time estimates all point to a quantitatively
similar relationship between
subjective well-being and income. This relationship is robust:
we find it not only at different
levels of aggregation but using different data sets. We also
find that income is positively
associated with other measures of subjective well-being,
including happiness as well as other
upbeat emotions.
The fact that life satisfaction and other measures of subjective
well-being rise with
income has significant implications for development economists.
First, and most importantly,
these findings cast doubt on the Easterlin Paradox and various
theories suggesting that there is no
long-term relationship between well-being and income growth.
Absolute income appears to play
a central role in determining subjective well-being. This
conclusion suggests that economists’
traditional interest in economic growth has not been misplaced.
Second, our results suggest that
differences in subjective well-being over time or across places
likely reflect meaningful
differences in actual well-being.
Subjective well-being data therefore permit cross-country
well-being comparisons
without reliance on price indexes. As Deaton (2010) notes, if we
wish to use some kind of
dollar-a-day threshold to count poverty, then we need price
indices that account for differences
in quality and in quantity of consumption in different
countries. In theory, constructing these
price indices is straightforward, provided one is ready to
assume identical homothetic
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31
preferences across countries. In practice, however, a central
challenge to creating price indices
is that many countries consume very different set of goods—there
is no price of smoked bonga in
some countries. When countries grow richer, previously
unavailable goods become traded as
very expensive specialty items. Paradoxically, as a country
grows richer, its poverty count can
grow because its prices are revised upward, devaluing
income.13
As Deaton suggests, many changes in PPP adjustments simply
involve better data, and
should not be ignored. But it can be difficult to know how much
of the changes in the poverty
count reflect actual changes in global poverty and how much
reflect updating of measurement
methods. In light of these difficulties, Deaton asks, “why don’t
we just ask people?” Using data
from 87 countries spanning 2006-2008, Deaton computes average
life satisfaction in each year in
the world. “For the world as a whole,” he writes, “2007 was a
better year than 2006; in 2008
more households reported being in difficulty and being
dissatisfied with their lives, and these
reports were worse still in 2009” (Deaton 2010, p. 30).
Deaton notes that these comparisons are only valid if life
satisfaction responds to
absolute rather than relative well-being. If individuals assess
their life relative to contemporary
standards, then as countries and the world grow richer, reported
satisfaction may not change.
However, our analysis suggests an important role for absolute
income in determining life
satisfaction, therefore we conclude that subjective well-being
data is indeed likely to be useful in
assessing trends in global well-being.
Finally, we should note that we have focused on establishing the
magnitude of the
relationship between subjective well-being and income, rather
than disentangling causality from
correlation. The causal impact of income on individual or
national subjective well-being, and the
13 As Deaton notes, adjusting for this difficulty is in theory
straightforward: weight goods by whether they are
considered luxury items. This task may be quite difficult,
however, because it requires making a judgment about
many thousands of goods for each country in the world.
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32
mechanisms by which income raises subjective well-being, remain
open and important
questions.
-
References—1
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References—2
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-
Tables—1
Table 1: Within-Country Satisfaction-Income Gradient
Dependent variable:
Standardized Life satisfaction
Without
controls
With
controls
Permanent Income
Adjusted
Instrumental
Variables
Sample size
Gallup World Poll: Ladder
question
0.236***
(0.014)
0.232***
(0.014)
0.422 0.449 ***
(0.027)
171,900
(126 Countries)
World Values Survey:
Life satisfaction
0.216***
(0.017)
0.227***
(0.037)
0.413 0.26***
(0.035)
116,527
(61 Countries)
Pew Global Attitudes Survey:
Ladder question
0.281***
(0.027)
0.283***
(0.027)
0.515 0.393***
(0.033)
32,463
(43 Countries)
Notes: The table reports the coefficient on the log of household
income, obtained from regressing standardized life
satisfaction against the log of household income and country
fixed effects using the indicated data set. Additional
controls include a quartic in age, interacted with sex, plus
indicators for age and sex missing. Our permanent income
adjustment is to scale up our estimates by 1/0.55; see text for
explanation. We instrument for income using full set
of country×education fixed effects. We report robust standard
errors, clustered at the country level, in parentheses.
For further details on the standardization of satisfaction and
the exact wording of satisfaction question, see the text. ***, **
and * denote statistically significant at 1%, 5% and 10%,
respectively.
-
Tables—2
Table 2: Cross-Country Regressions of Life Satisfaction on Log
GDP per Capitaa
Microdata National Data
Dependent variable:
Standardized life satisfaction
Without
controls
With
controls
Sample size
Gallup World Poll: Ladder
question
0.357***
(0.019)
0.378***
(0.019)
0.342***
(0.019)
291,383
(131 countries)
World Values Survey:
Life satisfaction
0.360***
(0.034)
0.364***
(0.034)
0.370***
(0.036)
234,093
(79 countries)
Pew Global Attitudes
Survey: Ladder question
0.214***
(0.039)
0.231***
(0.038)
0.204***
(0.037)
37,974
(44 countries)
Notes: The table reports the coefficient on the log of per
capita GDP, obtained from regressing standardized life
satisfaction against the log of GDP, using individual data with
and without controls, and using national-level data
without controls, in the indicated data set. In the
national-level regressions, we take the within-country average
of
standardized life satisfaction as the dependent variable. GDP
per capita is at purchasing power parity. The additional
controls include a quartic in age, interacted with sex, plus
indicators for age and sex missing. We report robust
standard errors, clustered at the country level, in parentheses.
For further details on the standardization of
satisfaction, the exact wording of satisfaction question, and
the sources for GDP per capita, see the text. ***, ** and *
denote statistically significant at 1%, 5% and 10%,
respectively.
-
Tables—3
Table 3: Time Series Regressions of Life Satisfaction on GDP per
Capitaa
Dependent variable:
Standardized life
satisfaction
WVS:
All Countries
WVS:
Transition
Countries
WVS:
Non-transition
Countries
Eurobarometer:
All Countries
Panel A: Panel Regressions
ln(GDP) 0.505***
(0.109)
0.628**
(0.239)
0.407***
(0.116)
0.17**
(0.074)
N 166 observations
79 countries
31 observations
10 countries
135 observations
66 countries
776 observations
31 countries
Panel B: Long differences
ln(GDP) 0.47***
(0.128)
0.694*
(0.387)
0.35**
(0.163)
0.278*
(0.164)
N 66 differences
10 differences 46 differences 30 differences
Notes: The table reports the coefficient on the log of GDP per
capita. In the panel regressions, we regress
standardized life satisfaction against the log of GDP per capita
as well as wave and country fixed effects. In
the long differences, we regress the change in standardized
satisfaction against the change in log GDP per
capita, after adjusting satisfaction and log GDP for wave and
country fixed effects. Long differences in the
World Values Survey are taken between the first and last time we
see a country; in the Eurobarometer,
between decadal averages. We report robust standard errors,
clustered at the country level, in parentheses. For
further details on the standardization of satisfaction, the
exact wording of satisfaction question, the sources for GDP
per capita, the procedure used to compute long differences, and
the definition of transition countries, see the text. . ***, ** and
* denote statistically significant at 1%, 5% and 10%,
respectively.
-
Figures–1
Figure 1: Relationship Between Well-being and Income, Within
Individual Countries,
Gallup World Poll
Notes: The figure shows, for the 25 largest countries, the
lowess fit between individual satisfaction ladder scores
and the log of household income, measured in the Gallup World
Poll. The satisfaction data are shown both on their
raw (0-10) scale on the left axis, and as standardized variables
on the right axis. We plot the lowess fit between the
10th and 90th percentiles of each country’s income distribution.
Satisfaction is assessed using the ladder of life
question.
CHN
IND
USA
BRA
PAKRUS
BGDNGA
JPN
MEX
PHL
VNM
DEU
EGY TURIRN
ETH
THA
FRA
GBR
ITA
KOR
UKR
ZAF
-1
-.5
0
.5
1
1.5
Sta
nd
ard
ized
sat
isfa
ctio
n l
add
er s
core
3
4
5
6
7
8
9
Sat
isfa
ctio
n l
add
er s
core
(0
-10)
.5 1 2 4 8 16 32 64 128Annual household income ($000s; Log
income scale)
-
Figures–2
Figure 2: Life Satisfaction and Real GDP per Capita, World
Values Survey
Notes: Respondents are asked, “All things considered, how
satisfied are you with your life as a whole these
days?”; respondents then choose a number from 1 (completely
dissatisfied) to 10 (completely satisfied). Data are
aggregated by first standardizing individual-level data to have
mean zero and unit standard deviation, and then
taking country-year averages of the standardized values. The
left axis gives the raw average satisfaction and the
right axis gives the standardized satisfaction. Dashed lines are
fitted from an OLS regression; dotted lines are fitted
from lowess regressions. These lines and the reported
regressions are fitted only from the nationally representative
samples. The units on the regression coefficients refer to the
normalized scale. Real GDP per capita is at
purchasing power parity in constant 2000 international dollars.
Sample includes 20 (1981-84) 42 (1989-93), 52
(1984-99) or 69 countries (1999-2004) from the World Values
Survey. Observations represented by hollow squares
are drawn from countries in which the World Values Survey sample
is not nationally representative (see Stevenson
and Wolfers (2008), appendix B, for more details).
AUS
BEL
CAN
DEU
DNK
ESP FRA
GBR
HUN
IRLISL
ITAJPN
KOR
MLTNLDNORSWEUSA
ARG
-1.5
-1.0
-0.5
0.0
0.5
1.0
3
4
5
6
7
8
9
.5 1 2 4 8 16 32
y = -4.07+0.46*ln(x) [se=0.22]Correlation=0.57
1981-84 wave
AUTBEL
BGR
BLR
BRA
CAN
CHE
CZEDEU
DNK
ESP
EST
FIN
FRA
GBR
HUN
IRL ISL
ITA
JPNKOR
LTULVA
MLT
NLDNOR
POL
PRT
ROM
RUS
SVKSVN
SWE
TUR
USA
ARGCHLCHNIND MEXNGA ZAF
-1.5
-1.0
-0.5
0.0
0.5
1.0
3
4
5
6
7
8
9
.5 1 2 4 8 16 32
y = -4.69+0.51*ln(x) [se=0.08]Correlation=0.74
1989-93 wave
ALB
ARM
AUS
AZE
BGR
BIH
BLR
BRA
CHECOL
CZE
DEUESP
EST
FINGBR
GEO
HRVHUN
JPN
LTULVA
MDA
MEX
MKD
NORNZL
PER
PHL
POL
PRI
ROMRUS
SCG
SLV
SVKSVN
SWE
TURTWN
UKR
URY
USA
VEN
ZAF
ARGBGD CHL
CHN
DOM
INDNGA
-1.5
-1.0
-0.5
0.0
0.5
1.0
3
4
5
6
7
8
9
.5 1 2 4 8 16 32
y = -4.00+0.43*ln(x) [se=0.05]Correlation=0.72
1994-99 wave
ALB
ARG
AUT
BEL
BGD
BGR
BIH
BLR
CAN
CHN
CZEDEU
DNK
DZA
ESP
EST
FIN
FRA
GBR
GRCHRV
HUN
IDN
IND
IRL
IRN
IRQ
ISL
ISRITA
JOR
JPNKGZKOR
LTU
LUX
LVA
MAR
MDA
MEX
MKD
MLT
NGA
NLD
PAK
PERPHL
POL
PRI
PRT
ROM
RUS
SAU
SCG
SGP
SVK
SVN
SWE
TUR
TZA
UGA
UKR
USAVEN
VNM
ZAF
ZWE
CHL
EGY
-1.5
-1.0
-0.5
0.0
0.5
1.0
3
4
5
6
7
8
9
.5 1 2 4 8 16 32
y = -2.88+0.32*ln(x) [se=0.04]Correlation=0.72
1999-2004 wave
Sta
nd
ard
ized
sat
isfa
ctio
n l
adder
sco
re
Sat
isfa
ctio
n l
add
er s
core
(0-1
0)
Real GDP per Capita (thousands of dollars, log scale)
-
Figures–3
Figure 3: Life Satisfaction and Real GDP per Capita, Pew Global
Attitudes Survey 2002
Notes: Respondents are shown a picture of a ladder with ten
steps and asked, “Here is a ladder representing the
‘ladder of life.’ Let's suppose the top of the ladder represents
the best possible life for you; and the bottom, the worst
possible life for you. On which step of the ladder do you feel
you personally stand at the present time?” Data are
aggregated by first standardizing individual-level data to have
mean zero and unit standard deviation, and then
taking country-year averages of the standardized values. The
left axis gives the raw average satisfaction and the
right axis gives the standardized satisfaction score. Dashed
lines are fitted from an OLS regression; dotted lines are
fitted from lowess regressions. Regression coefficients are in
terms of the standardized scaling. Real GDP per capita
is at purchasing power parity in constant 2000 international
dollars. Sample includes forty-four developed and
developing countries.
AGO
ARG
BGD
BGR
BOL
BRA
CAN
CHNCIV
CZE
DEUEGYFRAGBR
GHA
GTM
HND
IDN
IND
ITA
JOR
JPN
KEN
KOR
LBN
MEX
MLI
NGA
PAK
PER
PHL POL
RUS
SEN SVK
TUR
TZAUGA
UKR
USA
UZB
VENVNM
ZAF
-1.0
-0.5
0.0
0.5
1.0
1.5
Sta
ndar
diz
ed s
atis
fact
ion
lad
der
sco
re
3
4
5
6
7
8
9
Sat
isfa
ctio
n l
add
er s
core
(0
-10
)
.5 1 2 4 8 16 32Real GDP per capita (thousands of dollars, log
scale)
y = -1.73+0.20*ln(x) [se=0.04]Correlation=0.55
-
Figures–4
Figure 4: Life Satisfaction and Real GDP per Capita, Gallup
World Poll
Notes: Respondents are shown a picture of a ladder with ten
steps and asked, “Here is a ladder representing the
‘ladder of life.’ Let's suppose the top of the ladder represents
the best possible life for you; and the bottom, the worst
possible life for you. On which step of the ladder do you feel
you personally stand at the present time?” Data are
aggregated by first standardizing individual-level data to have
mean zero and unit standard deviation, and then
taking country-year averages of the standardized values. Dashed
lines are fitted from an OLS regression; dotted
lines are fitted from lowess regressions. The units on the
regression coefficients refer to the normalized scale. Real
GDP per capita is at purchasing power parity in constant 2000
international dollars. Sample includes 131 developed
and developing countries.
AFG
AGOALB
ARE
AR